No abstract available.
Population Characteristics*

No abstract available.
Population Characteristics

No abstract available.
Population Characteristics*

No abstract available.
Population Characteristics*

It is interested in examining treatment effect on a particular category of subjects or in comparing treatment effects among different subgroups as well as overall treatment effect due to heterogeneity of study subjects. Subgroup analyses are exceedingly common, but they are also often misleading. Conclusions based on subgroup analyses can do harm both when a particular category of people is denied effective treatment (a "false-negative" conclusion), and when ineffective or even harmful treatment is given to a subgroup of people (a "false-positive" conclusion). Because of the frequency and the importance of clinical application of subgroup analysis, researchers need to be cautious about doing subgroup analyses. This study presents guidelines to help conducting subgroup analyses correctly.
Population Characteristics

The Mechanism for sorting out the covariance effect is known as the covariance analysis. The sorting out of regression and correlation effect is an obvious application of the covariance analysis. The result of Do population by age groups (15 age interval) from 1966 census and from, 1970 Census has been applied to analyzing covariabiltiy by the analysis of covariance. The results are as follows. The significance of the regression of 1970 population on 1966 population is assured as F=116.5. There is a significant difference between mean of each age groups(F=88.1). There is very little evidence of significant heterogeneity of regression between age group. (F=0.72).
Censuses ; Humans ; Population Characteristics

Radiomics utilizes high-dimensional imaging data to discover the association with diagnostic, prognostic, predictive endpoint or radiogenomics. It is an emerging field of study that potentially depicts the intratumoral heterogeneity from quantitative and classified high-throughput data. The radiomics approach has an analytic pipeline where the imaging features are extracted, processed and analyzed. At this point, special data handling is essential because it faces issues of a high-dimensional biomarker compared to a single biomarker approach. This article describes the potential role of radiomics in oncologic studies, the basic analytic pipeline and special data handling with high-dimensional data to facilitate the radiomics approach as a tool for personalized medicine in oncology.
Population Characteristics ; Precision Medicine

No abstract available.
Parkinson Disease* ; Population Characteristics*

No abstract available.
Animals ; Nephrons* ; Population Characteristics* ; Rats*

No abstract available.
Population Characteristics* ; Thyroid Gland* ; Thyroiditis*